{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext zipline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period_open</th>\n",
       "      <th>period_close</th>\n",
       "      <th>long_value</th>\n",
       "      <th>short_value</th>\n",
       "      <th>long_exposure</th>\n",
       "      <th>pnl</th>\n",
       "      <th>short_exposure</th>\n",
       "      <th>capital_used</th>\n",
       "      <th>orders</th>\n",
       "      <th>transactions</th>\n",
       "      <th>...</th>\n",
       "      <th>beta</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>sortino</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>max_leverage</th>\n",
       "      <th>excess_return</th>\n",
       "      <th>treasury_period_return</th>\n",
       "      <th>trading_days</th>\n",
       "      <th>period_label</th>\n",
       "      <th>algorithm_period_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-03 21:00:00+00:00</th>\n",
       "      <td>2011-01-03 14:31:00+00:00</td>\n",
       "      <td>2011-01-03 21:00:00+00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[{'id': '6a455e6ab0ec419eae3750a20ee58fa6', 'd...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04 21:00:00+00:00</th>\n",
       "      <td>2011-01-04 14:31:00+00:00</td>\n",
       "      <td>2011-01-04 21:00:00+00:00</td>\n",
       "      <td>3312.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3312.90</td>\n",
       "      <td>-1.666450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3314.566450</td>\n",
       "      <td>[{'id': '6a455e6ab0ec419eae3750a20ee58fa6', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2011-01-04 21:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>-11.224972</td>\n",
       "      <td>-11.224972</td>\n",
       "      <td>-1.666450e-07</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-01</td>\n",
       "      <td>-1.666450e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05 21:00:00+00:00</th>\n",
       "      <td>2011-01-05 14:31:00+00:00</td>\n",
       "      <td>2011-01-05 21:00:00+00:00</td>\n",
       "      <td>6680.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6680.00</td>\n",
       "      <td>25.420000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3341.680000</td>\n",
       "      <td>[{'id': '3cb6afd62e6a41edae09f7622bd6eec8', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2011-01-05 21:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>8.279999</td>\n",
       "      <td>130.639936</td>\n",
       "      <td>-1.666450e-07</td>\n",
       "      <td>0.000668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-01</td>\n",
       "      <td>2.375355e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06 21:00:00+00:00</th>\n",
       "      <td>2011-01-06 14:31:00+00:00</td>\n",
       "      <td>2011-01-06 21:00:00+00:00</td>\n",
       "      <td>10011.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10011.90</td>\n",
       "      <td>-7.078650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3338.978650</td>\n",
       "      <td>[{'id': '30e39a096f1547ac8d5a0963cbd34c3c', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2011-01-06 21:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>4.568256</td>\n",
       "      <td>18.200004</td>\n",
       "      <td>-7.078633e-07</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-01</td>\n",
       "      <td>1.667490e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07 21:00:00+00:00</th>\n",
       "      <td>2011-01-07 14:31:00+00:00</td>\n",
       "      <td>2011-01-07 21:00:00+00:00</td>\n",
       "      <td>13444.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13444.80</td>\n",
       "      <td>70.009400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3362.890600</td>\n",
       "      <td>[{'id': '5e0c54dbcf084194bc4d8f0497958ce8', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2011-01-07 21:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>8.598826</td>\n",
       "      <td>84.623827</td>\n",
       "      <td>-7.078633e-07</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-01</td>\n",
       "      <td>8.668430e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-24 18:00:00+00:00</th>\n",
       "      <td>2012-12-24 14:31:00+00:00</td>\n",
       "      <td>2012-12-24 18:00:00+00:00</td>\n",
       "      <td>2585234.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2585234.96</td>\n",
       "      <td>4153.869160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5204.290840</td>\n",
       "      <td>[{'id': '6e1b7b072235490db292b6421eeee560', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2012-12-24 18:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0.299939</td>\n",
       "      <td>0.429102</td>\n",
       "      <td>-7.975863e-02</td>\n",
       "      <td>0.274341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>498</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>2.506420e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-26 21:00:00+00:00</th>\n",
       "      <td>2012-12-26 14:31:00+00:00</td>\n",
       "      <td>2012-12-26 21:00:00+00:00</td>\n",
       "      <td>2554740.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2554740.00</td>\n",
       "      <td>-35627.535000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5132.575000</td>\n",
       "      <td>[{'id': 'a3a4d015eb5546c3a4f4b139a7196bcc', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2012-12-26 21:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0.260329</td>\n",
       "      <td>0.371869</td>\n",
       "      <td>-7.975863e-02</td>\n",
       "      <td>0.274341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>499</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>2.150144e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-27 21:00:00+00:00</th>\n",
       "      <td>2012-12-27 14:31:00+00:00</td>\n",
       "      <td>2012-12-27 21:00:00+00:00</td>\n",
       "      <td>2570149.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2570149.40</td>\n",
       "      <td>10256.214700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5153.185300</td>\n",
       "      <td>[{'id': 'bb68ae122c894ed5a595ca640ba9d380', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2012-12-27 21:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0.271249</td>\n",
       "      <td>0.387512</td>\n",
       "      <td>-7.975863e-02</td>\n",
       "      <td>0.274341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>2.252707e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-28 21:00:00+00:00</th>\n",
       "      <td>2012-12-28 14:31:00+00:00</td>\n",
       "      <td>2012-12-28 21:00:00+00:00</td>\n",
       "      <td>2547945.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2547945.00</td>\n",
       "      <td>-27302.847945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5098.447945</td>\n",
       "      <td>[{'id': 'f4cc85ff60944420ab598b7bed66922e', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2012-12-28 21:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0.240977</td>\n",
       "      <td>0.343959</td>\n",
       "      <td>-7.993422e-02</td>\n",
       "      <td>0.274341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>501</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>1.979678e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00+00:00</th>\n",
       "      <td>2012-12-31 14:31:00+00:00</td>\n",
       "      <td>2012-12-31 21:00:00+00:00</td>\n",
       "      <td>2666186.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2666186.73</td>\n",
       "      <td>112917.329135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5324.400865</td>\n",
       "      <td>[{'id': 'edb5dfe5fdab4aa2bca10f2824b893c2', 'd...</td>\n",
       "      <td>[{'amount': 10, 'dt': 2012-12-31 21:00:00+00:0...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0.358657</td>\n",
       "      <td>0.519560</td>\n",
       "      <td>-7.993422e-02</td>\n",
       "      <td>0.274341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>502</td>\n",
       "      <td>2012-12</td>\n",
       "      <td>3.108851e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>502 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        period_open              period_close  \\\n",
       "2011-01-03 21:00:00+00:00 2011-01-03 14:31:00+00:00 2011-01-03 21:00:00+00:00   \n",
       "2011-01-04 21:00:00+00:00 2011-01-04 14:31:00+00:00 2011-01-04 21:00:00+00:00   \n",
       "2011-01-05 21:00:00+00:00 2011-01-05 14:31:00+00:00 2011-01-05 21:00:00+00:00   \n",
       "2011-01-06 21:00:00+00:00 2011-01-06 14:31:00+00:00 2011-01-06 21:00:00+00:00   \n",
       "2011-01-07 21:00:00+00:00 2011-01-07 14:31:00+00:00 2011-01-07 21:00:00+00:00   \n",
       "...                                             ...                       ...   \n",
       "2012-12-24 18:00:00+00:00 2012-12-24 14:31:00+00:00 2012-12-24 18:00:00+00:00   \n",
       "2012-12-26 21:00:00+00:00 2012-12-26 14:31:00+00:00 2012-12-26 21:00:00+00:00   \n",
       "2012-12-27 21:00:00+00:00 2012-12-27 14:31:00+00:00 2012-12-27 21:00:00+00:00   \n",
       "2012-12-28 21:00:00+00:00 2012-12-28 14:31:00+00:00 2012-12-28 21:00:00+00:00   \n",
       "2012-12-31 21:00:00+00:00 2012-12-31 14:31:00+00:00 2012-12-31 21:00:00+00:00   \n",
       "\n",
       "                           long_value  short_value  long_exposure  \\\n",
       "2011-01-03 21:00:00+00:00        0.00          0.0           0.00   \n",
       "2011-01-04 21:00:00+00:00     3312.90          0.0        3312.90   \n",
       "2011-01-05 21:00:00+00:00     6680.00          0.0        6680.00   \n",
       "2011-01-06 21:00:00+00:00    10011.90          0.0       10011.90   \n",
       "2011-01-07 21:00:00+00:00    13444.80          0.0       13444.80   \n",
       "...                               ...          ...            ...   \n",
       "2012-12-24 18:00:00+00:00  2585234.96          0.0     2585234.96   \n",
       "2012-12-26 21:00:00+00:00  2554740.00          0.0     2554740.00   \n",
       "2012-12-27 21:00:00+00:00  2570149.40          0.0     2570149.40   \n",
       "2012-12-28 21:00:00+00:00  2547945.00          0.0     2547945.00   \n",
       "2012-12-31 21:00:00+00:00  2666186.73          0.0     2666186.73   \n",
       "\n",
       "                                     pnl  short_exposure  capital_used  \\\n",
       "2011-01-03 21:00:00+00:00       0.000000             0.0      0.000000   \n",
       "2011-01-04 21:00:00+00:00      -1.666450             0.0  -3314.566450   \n",
       "2011-01-05 21:00:00+00:00      25.420000             0.0  -3341.680000   \n",
       "2011-01-06 21:00:00+00:00      -7.078650             0.0  -3338.978650   \n",
       "2011-01-07 21:00:00+00:00      70.009400             0.0  -3362.890600   \n",
       "...                                  ...             ...           ...   \n",
       "2012-12-24 18:00:00+00:00    4153.869160             0.0  -5204.290840   \n",
       "2012-12-26 21:00:00+00:00  -35627.535000             0.0  -5132.575000   \n",
       "2012-12-27 21:00:00+00:00   10256.214700             0.0  -5153.185300   \n",
       "2012-12-28 21:00:00+00:00  -27302.847945             0.0  -5098.447945   \n",
       "2012-12-31 21:00:00+00:00  112917.329135             0.0  -5324.400865   \n",
       "\n",
       "                                                                      orders  \\\n",
       "2011-01-03 21:00:00+00:00  [{'id': '6a455e6ab0ec419eae3750a20ee58fa6', 'd...   \n",
       "2011-01-04 21:00:00+00:00  [{'id': '6a455e6ab0ec419eae3750a20ee58fa6', 'd...   \n",
       "2011-01-05 21:00:00+00:00  [{'id': '3cb6afd62e6a41edae09f7622bd6eec8', 'd...   \n",
       "2011-01-06 21:00:00+00:00  [{'id': '30e39a096f1547ac8d5a0963cbd34c3c', 'd...   \n",
       "2011-01-07 21:00:00+00:00  [{'id': '5e0c54dbcf084194bc4d8f0497958ce8', 'd...   \n",
       "...                                                                      ...   \n",
       "2012-12-24 18:00:00+00:00  [{'id': '6e1b7b072235490db292b6421eeee560', 'd...   \n",
       "2012-12-26 21:00:00+00:00  [{'id': 'a3a4d015eb5546c3a4f4b139a7196bcc', 'd...   \n",
       "2012-12-27 21:00:00+00:00  [{'id': 'bb68ae122c894ed5a595ca640ba9d380', 'd...   \n",
       "2012-12-28 21:00:00+00:00  [{'id': 'f4cc85ff60944420ab598b7bed66922e', 'd...   \n",
       "2012-12-31 21:00:00+00:00  [{'id': 'edb5dfe5fdab4aa2bca10f2824b893c2', 'd...   \n",
       "\n",
       "                                                                transactions  \\\n",
       "2011-01-03 21:00:00+00:00                                                 []   \n",
       "2011-01-04 21:00:00+00:00  [{'amount': 10, 'dt': 2011-01-04 21:00:00+00:0...   \n",
       "2011-01-05 21:00:00+00:00  [{'amount': 10, 'dt': 2011-01-05 21:00:00+00:0...   \n",
       "2011-01-06 21:00:00+00:00  [{'amount': 10, 'dt': 2011-01-06 21:00:00+00:0...   \n",
       "2011-01-07 21:00:00+00:00  [{'amount': 10, 'dt': 2011-01-07 21:00:00+00:0...   \n",
       "...                                                                      ...   \n",
       "2012-12-24 18:00:00+00:00  [{'amount': 10, 'dt': 2012-12-24 18:00:00+00:0...   \n",
       "2012-12-26 21:00:00+00:00  [{'amount': 10, 'dt': 2012-12-26 21:00:00+00:0...   \n",
       "2012-12-27 21:00:00+00:00  [{'amount': 10, 'dt': 2012-12-27 21:00:00+00:0...   \n",
       "2012-12-28 21:00:00+00:00  [{'amount': 10, 'dt': 2012-12-28 21:00:00+00:0...   \n",
       "2012-12-31 21:00:00+00:00  [{'amount': 10, 'dt': 2012-12-31 21:00:00+00:0...   \n",
       "\n",
       "                           ...  beta     sharpe     sortino  max_drawdown  \\\n",
       "2011-01-03 21:00:00+00:00  ...  None        NaN         NaN  0.000000e+00   \n",
       "2011-01-04 21:00:00+00:00  ...  None -11.224972  -11.224972 -1.666450e-07   \n",
       "2011-01-05 21:00:00+00:00  ...  None   8.279999  130.639936 -1.666450e-07   \n",
       "2011-01-06 21:00:00+00:00  ...  None   4.568256   18.200004 -7.078633e-07   \n",
       "2011-01-07 21:00:00+00:00  ...  None   8.598826   84.623827 -7.078633e-07   \n",
       "...                        ...   ...        ...         ...           ...   \n",
       "2012-12-24 18:00:00+00:00  ...  None   0.299939    0.429102 -7.975863e-02   \n",
       "2012-12-26 21:00:00+00:00  ...  None   0.260329    0.371869 -7.975863e-02   \n",
       "2012-12-27 21:00:00+00:00  ...  None   0.271249    0.387512 -7.975863e-02   \n",
       "2012-12-28 21:00:00+00:00  ...  None   0.240977    0.343959 -7.993422e-02   \n",
       "2012-12-31 21:00:00+00:00  ...  None   0.358657    0.519560 -7.993422e-02   \n",
       "\n",
       "                           max_leverage  excess_return  \\\n",
       "2011-01-03 21:00:00+00:00      0.000000            0.0   \n",
       "2011-01-04 21:00:00+00:00      0.000331            0.0   \n",
       "2011-01-05 21:00:00+00:00      0.000668            0.0   \n",
       "2011-01-06 21:00:00+00:00      0.001001            0.0   \n",
       "2011-01-07 21:00:00+00:00      0.001344            0.0   \n",
       "...                                 ...            ...   \n",
       "2012-12-24 18:00:00+00:00      0.274341            0.0   \n",
       "2012-12-26 21:00:00+00:00      0.274341            0.0   \n",
       "2012-12-27 21:00:00+00:00      0.274341            0.0   \n",
       "2012-12-28 21:00:00+00:00      0.274341            0.0   \n",
       "2012-12-31 21:00:00+00:00      0.274341            0.0   \n",
       "\n",
       "                           treasury_period_return  trading_days  period_label  \\\n",
       "2011-01-03 21:00:00+00:00                     0.0             1       2011-01   \n",
       "2011-01-04 21:00:00+00:00                     0.0             2       2011-01   \n",
       "2011-01-05 21:00:00+00:00                     0.0             3       2011-01   \n",
       "2011-01-06 21:00:00+00:00                     0.0             4       2011-01   \n",
       "2011-01-07 21:00:00+00:00                     0.0             5       2011-01   \n",
       "...                                           ...           ...           ...   \n",
       "2012-12-24 18:00:00+00:00                     0.0           498       2012-12   \n",
       "2012-12-26 21:00:00+00:00                     0.0           499       2012-12   \n",
       "2012-12-27 21:00:00+00:00                     0.0           500       2012-12   \n",
       "2012-12-28 21:00:00+00:00                     0.0           501       2012-12   \n",
       "2012-12-31 21:00:00+00:00                     0.0           502       2012-12   \n",
       "\n",
       "                           algorithm_period_return  \n",
       "2011-01-03 21:00:00+00:00             0.000000e+00  \n",
       "2011-01-04 21:00:00+00:00            -1.666450e-07  \n",
       "2011-01-05 21:00:00+00:00             2.375355e-06  \n",
       "2011-01-06 21:00:00+00:00             1.667490e-06  \n",
       "2011-01-07 21:00:00+00:00             8.668430e-06  \n",
       "...                                            ...  \n",
       "2012-12-24 18:00:00+00:00             2.506420e-02  \n",
       "2012-12-26 21:00:00+00:00             2.150144e-02  \n",
       "2012-12-27 21:00:00+00:00             2.252707e-02  \n",
       "2012-12-28 21:00:00+00:00             1.979678e-02  \n",
       "2012-12-31 21:00:00+00:00             3.108851e-02  \n",
       "\n",
       "[502 rows x 38 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%zipline --start=2011-1-1 --end=2013-1-1 --no-benchmark\n",
    "\n",
    "from zipline.api import order, record, symbol\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def initialize(context):\n",
    "    pass\n",
    "\n",
    "def handle_data(context, data):\n",
    "    order(symbol('AAPL'), 10)\n",
    "    record(AAPL=data.current(symbol('AAPL'), \"price\"))\n",
    "    \n",
    "def analyze(context, perf):\n",
    "    ax1 = plt.subplot(211)\n",
    "    perf.portfolio_value.plot(ax=ax1)\n",
    "    ax2 = plt.subplot(212, sharex=ax1)\n",
    "    perf.AAPL.plot(ax=ax2)\n",
    "    plt.gcf().set_size_inches(18, 8)\n",
    "    plt.show()"
   ]
  }
 ],
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